Various techniques have been proposed and are currently in use for analyzing and compressing large data files, such as image data files. Image data files typically include streams of data descriptive of image characteristics, typically of intensities or other characteristics of individual pixel elements or pixels in a reconstructed image. In the medical field, for example, large image files are typically created during an image acquisition or encoding sequence, such as in an x-ray system, a magnetic resonance imaging system, a computed tomography imaging system, and so forth. The image data is then processed, such as to adjust dynamic ranges, enhance certain features shown in the image, and so forth, for storage, transmittal and display.
While image files may be stored in raw and processed formats, many image files are quite large, and would occupy considerable memory space. The increasing complexity of imaging systems also has led to the creation of very large image files, typically including more data as a result of the useful dynamic range of the imaging system and the size of the matrix of image pixels.
In addition to occupying large segments of available memory, large image files can be difficult or time consuming to transmit from one location to another. In a typical medical imaging application, for example, a scanner or other imaging device will typically create raw data which may be at least partially processed at the scanner. The data is then transmitted to other image processing circuitry, typically including a programmed computer, where the image data is further processed and enhanced. Ultimately, the image data is stored either locally at the system, or in a picture archiving and communications system (PACS) for later retrieval and analysis. In all of these data transmission steps, the large image data file must be accessed and transmitted from one device to another.
There is presently a need for improved image compression and decompression techniques which offer shorter compression and decompression times, while providing optimal compression of image data. There is a particular need for a lossless data compression technique which can be applied to a variety of image data types, particularly in the field of medical diagnostic imaging, for handling large data sets in an efficient manner from the points of view of computational requirements, memory requirements, and transmission bandwidth requirements.